language: en

Interpretable Neural Networks with BP-SOM

Ton Weijters 1 , Antal van den Bosch 2 , and Jaap van den Herik 3
1 Information Technology, Eindhoven University of Technology,
The Netherlands
2 ILK / Computational Linguistics, Tilburg University,
The Netherlands
3 Department of Computer Science, Universiteit Maastricht,
The Netherlands

Abstract. Interpretation of models induced by artificial neural networks is often a difficult task. In this paper we focus on a relatively novel neural network architecture and learning algorithm, bp-som, that offers possibilities to overcome this difficulty. It is shown that networks trained with bp-som show interesting regularities, in that hidden-unit activations become restricted to discrete values, and that the som part can be exploited for automatic rule extraction.

1 Introduction